# Hugging Face AI Models, Datasets & Spaces Scraper (`litezero/huggingface-models-scraper`) Actor

Extract machine learning models, download metrics, heart counts, pipeline tags (NLP, Vision, Audio), license types, and model card details from Hugging Face.

- **URL**: https://apify.com/litezero/huggingface-models-scraper.md
- **Developed by:** [LiteZero](https://apify.com/litezero) (community)
- **Categories:** AI, Developer tools, Open source
- **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks
- **User rating**: No ratings yet

## Pricing

from $0.84 / 1,000 item scrapeds

This Actor is paid per event. You are not charged for the Apify platform usage, but only a fixed price for specific events.
Since this Actor supports Apify Store discounts, the price gets lower the higher subscription plan you have.

Learn more: https://docs.apify.com/actors/running/actors-in-store.md#pay-per-event

## What's an Apify Actor?

An Actor is a serverless cloud program that runs on the Apify platform. It has two run modes.
In Batch mode, an Actor accepts a well-defined JSON input, performs an action which can take anything from a few seconds to a few hours,
and optionally produces a well-defined JSON output, datasets with results, or files in key-value store.
In Standby mode, an Actor provides a web server which can be used as a website, API, or an MCP server.

Apify vocabulary and the platform model are defined once, in the agent quickstart at https://apify.com/agents.md.

## How to integrate an Actor?

If asked about integration, you help developers integrate Actors into their projects.
You adapt to their stack and deliver integrations that are safe, well-documented, and production-ready.

Do not guess an integration path. Every one of them is in the agent quickstart at https://apify.com/agents.md: the Apify MCP server, Agent Skills with the Apify CLI, the JavaScript and Python clients, the REST API, and the account-free path for an agent with no human to sign in. It also carries the rule on stating cost before the first paid run.

For examples already wired to this Actor's own input schema, see the [API](#api) section below.

Each client library has reference documentation the quickstart does not restate: [JavaScript/TypeScript](https://docs.apify.com/api/client/js/docs.md) (`npm install apify-client`) and [Python](https://docs.apify.com/api/client/python/docs.md) (`pip install apify-client`).

# README

## 🤗 Hugging Face AI Models & Datasets Scraper

Scrape trending machine learning models, download metrics, likes, tasks, licenses, authors, and metadata directly from the Hugging Face Hub without API keys or rate limits.

***

### 🥊 Why This Actor Beats the Competition

| Feature | 🤗 Hugging Face Models Scraper | Manual Hub Browsing | Generic Web Scrapers |
| :--- | :--- | :--- | :--- |
| **Speed** | **200+ models in < 2 seconds** | Manual copy-pasting | 10–30s with heavy DOM rendering |
| **Pricing** | **Pay-Per-Result ($1.20 / 1k models)** | Free manual labor | Costly headless browser compute |
| **Deep Filtering** | By pipeline tag, downloads, likes, trending score | Basic UI search | Fragile CSS selectors |
| **Complete Metadata** | Licenses, tags, gating status, commit timestamps | Partial card data | Missing library names |
| **AI Stack Ready** | Structured JSON for GPU provisioning & model monitors | Web page only | Unclean HTML tags |

***

### 🛠️ Step-by-Step: How to Use

#### Method 1: Apify Console (No-Code GUI)

1. Select your target **Task / Pipeline Tag** (e.g. `text-generation`, `image-to-image`, or leave blank for all).
2. Choose a sort order: **Most Downloads**, **Most Likes**, or **Trending Score**.
3. Set your maximum model limit (default 25) and click **Start**.

#### Method 2: Python SDK

```python
from apify_client import ApifyClient

client = ApifyClient("YOUR_APIFY_TOKEN")
run_input = {
    "pipelineTag": "text-generation",
    "sort": "downloads",
    "limit": 20
}

run = client.actor("litezero/huggingface-models-scraper").call(run_input=run_input)
for item in client.dataset(run["defaultDatasetId"]).iterate_items():
    print(f"{item['modelId']} - {item['downloads']:,} downloads (License: {item['license']})")
```

#### Method 3: Node.js SDK

```javascript
import { ApifyClient } from 'apify-client';

const client = new ApifyClient({ token: 'YOUR_APIFY_TOKEN' });
const run = await client.actor('litezero/huggingface-models-scraper').call({
    pipelineTag: 'text-generation',
    sort: 'trendingScore',
    limit: 10,
});

const { items } = await client.dataset(run.defaultDatasetId).listItems();
console.log(items);
```

#### Method 4: cURL / REST API

```bash
curl --request POST \
  --url "https://api.apify.com/v2/acts/litezero~huggingface-models-scraper/runs?token=YOUR_APIFY_TOKEN" \
  --header "Content-Type: application/json" \
  --data '{"pipelineTag": "text-generation", "limit": 15}'
```

***

### 📥 Input Parameters

| Parameter | Type | Default | Description |
| :--- | :--- | :--- | :--- |
| `pipelineTag` | String | `"text-generation"` | Machine learning task (e.g. `text-generation`, `automatic-speech-recognition`, or `all`). |
| `sort` | Select | `"downloads"` | Sorting method (`downloads`, `likes`, `trendingScore`, `createdAt`, `lastModified`). |
| `searchQuery` | String | `""` | Optional keyword filter (e.g. `llama`, `whisper`, `deepseek`). |
| `limit` | Integer | `25` | Total number of models to retrieve. |

***

### 📤 Sample Output JSON

```json
{
  "modelId": "meta-llama/Meta-Llama-3.1-8B-Instruct",
  "author": "meta-llama",
  "pipelineTag": "text-generation",
  "downloads": 1420580,
  "downloadsAllTime": 4820194,
  "likes": 8412,
  "trendingScore": 96.4,
  "libraryName": "transformers",
  "license": "llama3.1",
  "tags": ["text-generation", "pytorch", "llama", "conversational"],
  "private": false,
  "gated": true,
  "lastModified": "2026-08-15T18:12:00.000Z",
  "modelUrl": "https://huggingface.co/meta-llama/Meta-Llama-3.1-8B-Instruct",
  "scrapedAt": "2026-10-02T16:45:00.000Z"
}
```

***

### 💡 Top Commercial Use Cases

1. **GPU Cloud Intelligence:** Track breakthrough models gaining download momentum to optimize hardware deployments.
2. **AI Competitive Benchmarking:** Monitor enterprise release velocity (Meta, Mistral, Microsoft, Google, DeepSeek).
3. **Automated LLM Directory Portals:** Power live catalogs of open-source models with up-to-date licenses and metrics.

# Actor input Schema

## `pipelineTag` (type: `string`):

Filter by machine learning task (e.g. text-generation, text-to-image, automatic-speech-recognition, translation, or leave blank for all).

## `sort` (type: `string`):

Sorting criteria for models.

## `searchQuery` (type: `string`):

Optional search string (e.g. llama, mistral, whisper, flux).

## `limit` (type: `integer`):

Number of AI models to extract from Hugging Face.

## Actor input object example

```json
{
  "pipelineTag": "text-generation",
  "sort": "downloads",
  "searchQuery": "",
  "limit": 25
}
```

# Actor output Schema

## `results` (type: `string`):

No description

# API

You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup.

## JavaScript example

```javascript
import { ApifyClient } from 'apify-client';

// Initialize the ApifyClient with your Apify API token
// Replace the '<YOUR_API_TOKEN>' with your token
const client = new ApifyClient({
    token: '<YOUR_API_TOKEN>',
});

// Prepare Actor input
const input = {};

// Run the Actor and wait for it to finish
const run = await client.actor("litezero/huggingface-models-scraper").call(input);

// Fetch and print Actor results from the run's dataset (if any)
console.log('Results from dataset');
console.log(`💾 Check your data here: https://console.apify.com/storage/datasets/${run.defaultDatasetId}`);
const { items } = await client.dataset(run.defaultDatasetId).listItems();
items.forEach((item) => {
    console.dir(item);
});

// 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/js/docs

```

## Python example

```python
from apify_client import ApifyClient

# Initialize the ApifyClient with your Apify API token
# Replace '<YOUR_API_TOKEN>' with your token.
client = ApifyClient("<YOUR_API_TOKEN>")

# Prepare the Actor input
run_input = {}

# Run the Actor and wait for it to finish
run = client.actor("litezero/huggingface-models-scraper").call(run_input=run_input)

# Fetch and print Actor results from the run's dataset (if there are any)
print(f"💾 Check your data here: https://console.apify.com/storage/datasets/{run.default_dataset_id}")
for item in client.dataset(run.default_dataset_id).iterate_items():
    print(item)

# 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start

```

## CLI example

```bash
echo '{}' |
apify call litezero/huggingface-models-scraper --silent --output-dataset

```

## MCP server setup

```json
{
    "mcpServers": {
        "apify": {
            "type": "http",
            "url": "https://mcp.apify.com/?tools=fetch-actor-details,litezero/huggingface-models-scraper"
        }
    }
}
```

The hosted server signs you in with OAuth on first connect, so no API token belongs in this config. Clients without OAuth support can send an `Authorization: Bearer <APIFY_API_TOKEN>` header instead, using a token from API & Integrations in Apify Console (https://console.apify.com/settings/integrations).

## OpenAPI specification

Download the OpenAPI definition: https://api.apify.com/v2/actors/ImrSbN0UcB6kQy44B/builds/I5cZuhufsXrT7QuIq/openapi.json